SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

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Showing 4501–4510 of 15113 papers

TitleStatusHype
One-shot, Offline and Production-Scalable PID Optimisation with Deep Reinforcement Learning—0
Symbolic Distillation for Learned TCP Congestion ControlCode1
MEET: A Monte Carlo Exploration-Exploitation Trade-off for Buffer SamplingCode0
OSS Mentor A framework for improving developers contributions via deep reinforcement learning—0
Opportunistic Episodic Reinforcement Learning—0
Understanding the Evolution of Linear Regions in Deep Reinforcement LearningCode0
Reinforcement Learning and Bandits for Speech and Language Processing: Tutorial, Review and Outlook—0
Energy Pricing in P2P Energy Systems Using Reinforcement LearningCode1
Graph Reinforcement Learning-based CNN Inference Offloading in Dynamic Edge Computing—0
Causal Explanation for Reinforcement Learning: Quantifying State and Temporal Importance—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified